Rotating machinery is used in many applications. For example, machines such as mobile machines, e.g., on and off road vehicles, construction machines, earthworking machines, and the like, employ principles of rotation to function. Engines, motors, drive trains, ground engaging components such as wheels or tracks, and the like rotate to enable the machines to perform work tasks.
The efficiency and life expectancy of rotating machinery may be analyzed and determined by resort to a study of vibrations present in the machine components. Friction forces between moving parts, compounded by irregularities in component tolerances, serve to cause vibrations in the machines. An analysis of the vibrations may aid in determining, in real time and non-intrusively, the health of the machines, even to the point of predicting component life and potential breakdowns.
Vibration analysis, including related concepts of sound and ultrasonic analysis, has long been of interest in monitoring and diagnosing machine health. However, vibration analysis techniques have typically proven to be lacking by either providing questionable results or providing data that cannot be readily interpreted and understood.
Efforts have been made to use artificial intelligence techniques to test and analyze vibration of machines. For example, U.S. Pat. Nos. 5,566,092, 5,566,273, 5,602,761, 5,854,993, 6,236,950 and 6,539,319, all assigned to the present assignee, disclose variations in techniques for using neural networks to perform testing and analysis of machines, particularly with respect to vibration characteristics of the machines. Although the techniques embodied in the above patents have resulted in some degree of success, it is desired that further techniques be developed which offer greater reliability, robustness and precision in testing and analysis.
The present invention is directed to overcoming one or more of the problems as set forth above.